Biology subjects

Kwah, J. S.

Publications and source records attributed to Kwah, J. S..

3 recordsLinked to original sources

bioRxiv2025

Epitranscriptomic Dysregulation Underpins Artemisinin Mechanism of Action

Artemisinin has long been a first-line antimalarial. Yet, its mode of action is still poorly understood. Emergence of artemisinin-resistant strains highlight the importance of addressing this question so as to develop better drugs and overcome resistance. In this study, we performed RNA-sequencing and proteomics studies on artemisinin treated parasites indicated a striking difference in the codon-usage pattern of differentially translated genes. Using a liquid chromatography-coupled mass spectrometry (LC-MS)-based platform, we have quantified the full spectrum of modified ribonucleosides on tRNA in P. falciparum in response to the drug. We found that N6-threonyl-carbomyladenosine (t6A), a universal tRNA modification found at position 37 is hypomodified in response to artemisinin induced stress. Additionally, we also found that artemisinin treatment resulted in a downregulation of PfSua5, an enzyme involved in the t6A biosynthesis machinery. These findings provide new insights into how artemisinin works. More broadly, the findings exposes the tRNA epitranscriptome as a vulnerability in the parasite that can be exploited for new drugs.

microbiology↗

bioRxiv2020

BEEM-Static: Accurate inference of ecological interactions from cross-sectional metagenomic data

MotivationThe structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of metagenomic sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional metagenomic datasets for unravelling ecological structure in a scalable manner thus remains an open problem. MethodsWe present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. ResultsBenchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n=4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. ConclusionBEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of metagenomic data.

bioinformatics↗

bioRxiv2019

Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment

There is growing attention surrounding hospital acquired infections (HAIs) due to high associated healthcare costs, compounded by the scourge of widespread multi-antibiotic resistance. Although hospital environment disinfection is well acknowledged to be key for infection control, an understanding of colonization patterns and resistome profiles of environment-dwelling microbes is currently lacking. We report the first extensive genomic characterization of microbiomes (428), common HAI-associated microbes (891) and transmissible drug resistance cassettes (1435) in a tertiary hospital environment based on a 3-timepoint sampling (1 week and >1 year apart) of 179 sites from 45 beds. Deep shotgun metagenomic sequencing unveiled two distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human microbiome influenced environments that display corresponding patterns of divergence over space and time. To study common nosocomial pathogens that were typically present at low abundances, a combination of culture enrichment and long-read nanopore sequencing was used to obtain thousands of high contiguity genomes (2347), phage sequences (1693) and closed plasmids (5910), a significant fraction of which (>60%) are not represented in current sequence databases. These high-quality assemblies and metadata enabled a rich characterization of resistance gene combinations, phage diversity, plasmid architectures, and the dynamic nature of hospital environment resistomes and their reservoirs. Phylogenetic analysis identified multidrug resistant strains as being more widely distributed and stably colonizing across hospital sites. Further genomic comparisons with clinical isolates across multiple species supports the hypothesis that multidrug resistant strains can persist in the hospital environment for extended periods (>8 years) to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in the hospital environment and establishes the feasibility of systematic genomic surveys to help target resources more efficiently for preventing HAIs.

microbiology↗